dlcp2023:abstracts
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| dlcp2023:abstracts [21/06/2023 00:18] – [6. Preliminary results of neural network models in HiSCORE experiment] admin | dlcp2023:abstracts [05/03/2025 17:33] (current) – ↷ Links adapted because of a move operation 156.59.198.135 | ||
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| ====== Book of Abstracts ====== | ====== Book of Abstracts ====== | ||
| - | //Draft June5, 2023 // | + | |
| + | {{ dlcp2023: | ||
| ===== Plenary reports ===== | ===== Plenary reports ===== | ||
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| The paper presents preliminary results on determining the direction of the EAS axis in experiments representing an array of Cherenkov detectors. An example of such a facility is the HiSCORE facility deployed near Baikal lake as part of the TAIGA experiment. Two approaches have been considered. One is based on the representation of the time of registration of signal arrival by stations in the form of an image, which is processed using a convolutional network. Another approach is to allocate a subset of the same number of triggered stations in each events, which includes data on the location of stations relative to each other and the relative time of signal registration. The analysis is performed using a fully connected deep network. It was shown in the work that both approaches give approximately the same accuracy. In the future, we propose to optimize the architecture of both networks and the process of their training to improve the accuracy of predicting EAS parameters. | The paper presents preliminary results on determining the direction of the EAS axis in experiments representing an array of Cherenkov detectors. An example of such a facility is the HiSCORE facility deployed near Baikal lake as part of the TAIGA experiment. Two approaches have been considered. One is based on the representation of the time of registration of signal arrival by stations in the form of an image, which is processed using a convolutional network. Another approach is to allocate a subset of the same number of triggered stations in each events, which includes data on the location of stations relative to each other and the relative time of signal registration. The analysis is performed using a fully connected deep network. It was shown in the work that both approaches give approximately the same accuracy. In the future, we propose to optimize the architecture of both networks and the process of their training to improve the accuracy of predicting EAS parameters. | ||
| + | The work was supported by RSF, grant no.22-21-00442. The work was done using the data of UNU " | ||
| ==== 27. The use of conditional variational autoencoders for simulation of EASs images from IACTs ==== | ==== 27. The use of conditional variational autoencoders for simulation of EASs images from IACTs ==== | ||
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| //Poster// | //Poster// | ||
| - | __V.Kalninsky__ | + | __V.Kalnitsky__ |
| The problem of limited accuracy of machine learning models using soft logical connectives is investigated. Such connectives have shown their effectiveness in models with fuzzy initial data. On the one hand, the fundamental disadvantage of soft connectives is their non-associativity. On the other hand, the disadvantages of the currently used soft connectives include the loss of monotonicity and the inability to control several factors simultaneously. We have proposed an approximation of the signum function by a smooth spline. We are controlling the difference between the soft connective and the associative connective. It was shown that the spline approximation is able to reduce the influence of all negative factors and is more flexible in setting. Moreover, the constructed spline model allows numerous modifications depending on the factor that requires the most attention for different tasks. | The problem of limited accuracy of machine learning models using soft logical connectives is investigated. Such connectives have shown their effectiveness in models with fuzzy initial data. On the one hand, the fundamental disadvantage of soft connectives is their non-associativity. On the other hand, the disadvantages of the currently used soft connectives include the loss of monotonicity and the inability to control several factors simultaneously. We have proposed an approximation of the signum function by a smooth spline. We are controlling the difference between the soft connective and the associative connective. It was shown that the spline approximation is able to reduce the influence of all negative factors and is more flexible in setting. Moreover, the constructed spline model allows numerous modifications depending on the factor that requires the most attention for different tasks. | ||
dlcp2023/abstracts.1687295898.txt.gz · Last modified: 21/06/2023 00:18 by admin